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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Exploring the impact of nucleotide length on lipid nanoparticle structure and properties

Lipid nanoparticles (LNPs) are versatile carriers for nucleic acid (NA) therapeutics, including ASOs, siRNA, mRNA, and poly-IC. While lipid composition is known to influence LNP properties, the impact of NA length on morphology and internal structure is less understood, particularly during the stages of carrier–cargo assembly. Here, we examine NA length and lipid composition immediately after mixing using high-throughput SAXS, dynamic light scattering, and cryogenic electron microscopy. All LNPs form ordered NA/lipid compartments, with longer NAs promoting inverse hexagonal (H II ) phases and larger intercompartment distances. In contrast, short NAs, especially in formulations with SM102 ionizable lipid, favor lamellar phases. SAXS peak deconvolution quantifies ordered versus disordered phases via a Robustness of Ordered Phase factor, which correlates with particle size and encapsulation efficiency. Formulations with MC3 ionizable and DOPE helper lipids exhibit the most stable H II -phase packing, highlighting the role of helper-lipid curvature in compartment stabilization. Variations in NA compartmentalization indicate differences in payload capacity, offering a framework for rational LNP design across diverse nucleic acid cargos.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

High-Efficiency Solar-To-Fuel Photoelectrochemistry in Disordered Photonic Glass Electrodes (Final Technical Report)

This project investigated how photonic glass (PG) photoelectrodes—disordered arrangements of dielectric scatterers—can serve as scalable, tunable platforms for light trapping in photoelectrochemical (PEC) solar-to-fuel systems. By leveraging disorder-driven optical phenomena such as multiple scattering resonances and light localization, PG structures offer an alternative to conventional photonic crystals and inverse opals that require high structural precision. The scientific goals were to twofold: (1) develop approaches to predictive models for high performance PG electrodes based on light absorption simulations, and (2) fabricate, characterize, and optimize PG-based photoelectrodes for solar-to-hydrogen and solar-to-fuel photoelectrochemical applications. To overcome the complexity of ensemble optical simulations for disordered materials, the researchers developed a machine-learning-accelerated emulation of all configurations in the design space. With this approach, PG photoelectrodes based on a TiO2 semiconductor were designed to enhance PEC currents of up to one hundred times higher than the equivalent ultra-thin film photoanodes and several times higher than the equivalent photonic crystal. The research also explored integrated systems for electrochemical hydrogen production based on replacing water oxidation with the specific glycerol oxidation electrocatalysis. Overall, the project outlined an approach to a simple-to-fabricate photoelectrode system to drive photoelectrochemical reactions relevant to solar photochemical energy conversion.

14 SOLAR ENERGY↗
DOE OSTI2024

Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localization

Abstract This paper presents a statistical forward model for a Compton imaging system, called Compton imager. This system, under development at the University of Illinois Urbana Champaign, is a variant of Compton cameras with a single type of sensors which can simultaneously act as scatterers and absorbers. This imager is convenient for imaging situations requiring a wide field of view. The proposed statistical forward model is then used to solve the inverse problem of estimating the location and energy of point-like sources from observed data. This inverse problem is formulated and solved in a Bayesian framework by using a Metropolis within Gibbs algorithm for the estimation of the location, and an expectation-maximization algorithm for the estimation of the energy. This approach leads to more accurate estimation when compared with the deterministic standard back-projection approach, with the additional benefit of uncertainty quantification in the low photon imaging setting.

Tarpau, Cécilia (ORCID:0000000286539490)↗
DOE OSTI2025

Non-equilibrium anti-Stokes Raman spectroscopy for investigating Higgs modes in superconductors

Abstract Even before its role in electroweak symmetry breaking, the Anderson-Higgs mechanism was introduced to explain the Meissner effect in superconductors. Spontaneous symmetry-breaking yields massless phase modes representing the low-energy excitations of the Mexican-Hat potential. Only in superconductors the phase mode is shifted towards higher energies owing to the gauge field of the charged condensate. This results in a low-energy excitation spectrum governed by the Higgs mode. Consequently, the Bardeen-Cooper-Schrieffer-like Meissner effect signifies a macroscopic quantum condensate in which a photon acquires mass, representing a one-to-one analogy to high-energy physics. We report on an innovative spectroscopic technique to study symmetries and energies of the Higgs modes in the high-temperature superconductor Bi 2 Sr 2 CaCu 2 O 8 after a soft quench of the Mexican-Hat potential. Population inversion induced by an initial laser pulse leads to an additional anti-Stokes Raman-scattering signal, which is consistent with polarization-dependent Higgs modes. Within Ginzburg-Landau theory, the Higgs-mode energy is connected to the Cooper-pair coherence length. Within a Bardeen-Cooper-Schrieffer weak-coupling model we develop a quantitative and coherent description of single-particle and two-particle channels. This opens the avenue for Higgs Spectroscopy in quantum condensates and provides a unique pathway to control and explore Higgs physics.

Science & Technology - Other Topics↗
DOE OSTI2025

Overview of the SCEC/USGS Community Stress Drop Validation Study Using the 2019 Ridgecrest Earthquake Sequence

We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress-drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude-spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress-drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two-week period of the 2019 Ridgecrest sequence, recorded within a 1° radius. Here, in this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite-fault modeling, ground-motion-based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between-method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress-drop values.

58 GEOSCIENCES↗
DOE OSTI2025

Nondestructive Evaluation of Concrete: Elastic Property Imaging Through Full-Waveform Inversion

Concrete is a vital material in construction—especially in the nuclear industry, where it is used in critical structures such as containment vessels. Over time, concrete can degrade due to harsh operational and environmental conditions, necessitating that its elastic properties be accurately evaluated to ensure structural integrity and safety. Traditional nondestructive evaluation methods such as ultrasound-based techniques often rely on simplifying assumptions that may not hold true for concrete. This paper presents an advanced ultrasound-based method that uses elastic full-waveform inversion (EFWI) to create detailed images of concrete’s mechanical properties. By accurately modeling wave behaviors such as scattering and reflection, we aim to overcome the limitations of conventional ultrasonic-based methods. In this work, the imaging problem involved reconstructing the various elastic properties of a heterogenous concrete block with three steel rebars embedded in it. The ultrasonic measurements were synthetically generated from multiple sources and receivers, and the reconstruction process was performed using a gradient-based optimization algorithm. Our approach leveraged EFWI to reconstruct high-resolution images of the pressure wave speed, shear wave speed, and density. Multiple misfit functions—including L2-norm, cross-correlation (CC), and L1-norm—combined with total variation (TV) regularization and parameter constraints using a Sigmoid function—were explored for the reconstruction. The results demonstrated that using the L1-norm misfit function in conjunction with TV regularization and Sigmoid constraints significantly improved the reconstruction quality in comparison to traditional methods. This approach provided clearer images with fewer artifacts and better captured background heterogeneity. Our findings highlight that, when properly designed, EFWI carries great potential for providing comprehensive, more accurate, and more reliable assessments of concrete conditions, as is crucial for the maintenance and safety of nuclear power plant structures.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Identifying Topological Defects in Lamellar Phases through Contour Analysis of Complex Wave Fields

Lamellar phases frequently contain structural imperfections that significantly affect their behaviors and properties. Our previous research successfully reconstructed real-space configurations of defective lamellar phases from diffuse scattering patterns, indicating the presence of phase vortices as a potential method for identifying topological defects disrupting the smectic ordering. Here, this report presents a mathematical framework using regularized wave fields to represent defective lamellar structures in real space. Phase singularities, resulting from the interference of random waves and indicating lamellar order disruption, are identified through a contour integral. These wave fields, derived from coherent scattering in reciprocal space, were validated via computational benchmarks analyzing small-angle neutron scattering data from AOT surfactant solutions, facilitating further statistical analysis of the defects. Our study highlights the potential to extract meaningful information about topological defects in lyotropic phases by inversely analyzing experimentally measured two-point static correlations. Our method allows for detailed structural analysis of various lyotropic phases, both particulate and nonparticulate, in their quiescent states and facilitates quantitative investigation of defects’ role in phase transitions. By integrating small-angle scattering, deep learning, and vortex tangle analysis, our comprehensive approach shows promise in addressing complex challenges in the structural analysis of soft matter systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Uncovering hidden bias in neutron diffraction residual strain measurements

When calculating residual strain via neutron or X-ray diffraction, uncertainties propagated from the peak fit are often inadequate to describe the true scatter of measurements about a singular strain state, such as one that should describe a macroscopic continuum. Because diffraction is inherently a selective process, orientation-dependent scatter arises from the sub-sampling of strong microstructure and strain gradients. This paper investigates the appropriateness of propagated uncertainties with reference to their original intention, i.e. noise about a mean value. Thirty-six unique orientations of strain measurements are taken at multiple locations within an additive friction-stir deposition component with fine-scale gradients (∼200 µm) of plastic strain, texture and residual elastic strain. Multiple strain and stress calculation pathways are compared: direct substitution of three measurements into Hooke's law, direct inversion of any six unique orientations into the strain state tensor and thirty-six measurement least-squares estimation. For the last two cases, the appropriateness of the uncertainty interval is statistically evaluated on the basis of a physical constraint: common agreement under the strain transformation law. For this sample, the direct inversion of six measurements retains a conservative estimate of the uncertainty. However, propagated uncertainties in the least-squares solution greatly underestimate the true experimental scatter. A simple pathway to estimate appropriate uncertainty intervals is suggested. These results demonstrate that the interpretation of uncertainty in residual strain is strongly dependent on intrinsic sample-dependent effects, and that oversampling orientations and statistical analysis can give more accurate results with realistic uncertainties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nondestructive Evaluation of Concrete: Elastic Property Imaging Through Full Waveform Inversion

Concrete is a major construction material worldwide and plays a crucial role in the nuclear industry. The elastic properties of concrete are prone to change and degrade while in service, as it is often subjected to extreme operational and environmental conditions. An accurate evaluation of concrete's elastic properties is thus essential to ensure structural integrity and safety. This is especially true for concrete in nuclear power plants, where irradiation effects significantly impact concrete mechanical properties. There are various methods to assess these properties, with ultrasound-based techniques showing high potential due to their nondestructive nature, cost-effectiveness, and safety. While several nondestructive evaluation methods exist, most rely on idealizations such as assuming homogeneous material and plane wavefronts. In this work, we address these issues by introducing an ultrasound-based nondestructive method aimed at reconstructing spatially varying images of concrete mechanical properties. By accurately modeling wave physics, including scattering and reflection, we overcome several of the aforementioned idealizations and aim to utilize the full waveform for imaging material properties through depth, resulting in more reliable images. Full waveform inversion (FWI) was first introduced by geophysicists to reconstruct subsurface elastic property images. The goal is to minimize the difference between simulated and recorded wavefield signals, often through gradient-based optimization algorithms. While FWI is primarily conducted using the acoustic approximation of the wave equation, few works focus on elastic FWI, where the goal is to reconstruct images of not only the pressure wave speed but also the shear wave speed and density (or their equivalents). This work explores the potential of using elastic FWI to predict concrete mechanical properties as an initial effort for a more accurate monitoring of concrete conditions in service. Reconstructing images of different elastic parameters enables more specificity and accurate condition diagnosis. This paper will detail this approach and provide examples demonstrating the effectiveness of elastic FWI in reconstructing comprehensive maps of concrete mechanical properties.

42 - ENGINEERING↗
DOE OSTI2025

Experimental study of energy-dependent angular broadening of MeV electron beams for high-resolution imaging in thick samples

In scanning transmission electron microscopy (STEM), spatial resolution is primarily influenced by the projected size of the electron probe within the specimen. In thin samples, a large semi-convergence angle enables a tightly focused beam and sub-nanometer resolution. However, in thick specimens, resolution is fundamentally limited by transverse beam broadening from multiple large-angle scattering events—for example, a probe with 10 mrad angular divergence can broaden by ∼100 nm over a 10 μm path. Since this broadening scales inversely with beam energy, MeV-STEM offers a promising route for high-resolution imaging in thick materials. To quantitatively assess this effect, we performed high-precision measurements at UCLA’s PEGASUS beamline, characterizing beam divergence and intensity profiles for 3–8 MeV electrons transmitted through a wedged-silicon sample of varying thickness. Our results reconcile discrepancies among analytical models and validate Monte Carlo simulations. Here, we find that increasing beam energy from 3.0 to 5.8 MeV reduces angular broadening by a factor of 2.6, with diminishing returns observed at 7.6 MeV. These findings provide a quantitative framework for optimizing MeV-STEM parameters in high-resolution imaging of thick biological and microelectronic specimens, and for guiding beam energy selection in other advanced imaging modes beyond STEM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Significance of the structural configuration of B2 disorder in Co and Ti based Heusler alloys

We investigate here structural (at local and global levels) and transport properties for 𝑋 2 ⁢MnAl (𝑋= Co and Ti). Additionally, the magnetic properties were also studied for Ti 2 ⁢MnAl. Our x-ray diffraction results show that both the compounds stabilize in B2 disordered phase with cubic structure of 𝑃⁢𝑚⁢$\overline{3}$⁢𝑚 space group. Further, the structural configuration of the above disordered phase for both the compounds was identified using combined studies of x-ray absorption spectroscopy and multiple scattering calculations at the transition metal 𝐾 edges. Upon such identification, in the case of Co 2 ⁢Mn 1−𝑦⁢ Cr 𝑦 ⁢Al (𝑦= 0, 0.05, 0.1, 0.2) with change in 𝑦, we are able to establish a better connection quantitatively between the inverse of Mn-Co bonds and peak in the temperature-dependent resistivity. This highlights the crucial importance of a detailed understanding of the nature of B2 disorder. In the case of 𝑦=0, in the temperature range of study, the resistivity is driven by the functional form associated with (a) three-dimensional enhanced electron-electron Coulomb interaction scattering mechanism and (b) an unconventional one-magnon process. For Ti 2 ⁢MnAl, the transport shows metallic glasslike behavior at high temperature, while at low temperature it follows both the Cote-Meisel's model and quantum correction model. In this compound, the magnetic studies suggest the formation of superparamagnetic clusters in the paramagnetic matrix at low temperatures. Our density functional theory results are in line with the transport and magnetic properties. In literature, the spin polarization percentage (𝑃) for 𝑋= Co in B2 disordered phase is 76%. However, the present results emphasize the fact that in B2 disordered phase, the value of 𝑃 can range from 90% to 71% depending on the structural configuration introduced by swapping of the atomic positions of Mn and Al. For the compound under study, the value of percentage spin polarization obtained ranges between 82% to 85%. In addition, we also identify the origin of the difference in the shape of the Mn 3⁢𝑑 density of states for both the alloys. In conclusion, our results for 𝑋= Co alloy highlights the importance of identifying the specific structural configuration associated with a particular disorder category especially in the estimation of 𝑇 𝑐 and 𝑃 and for 𝑋= Ti, the physical properties can be tuned by varying the position of 𝐸 𝐹 and thereby its utilization in device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Measurement of Thomson-scattering spectra with continuous angular resolution (invited)

Here, a novel Thomson-scattering diagnostic with continuous angular resolution over a span of 120° was developed for the characterization of plasmas produced at the Omega Laser Facility. Spectrally resolving light scattered from electron plasma wave features as a function of emission angle provides a means to efficiently probe a large range of plasma frequencies and k vectors. Together, these spectra contain critical constraints on the plasma-physics models used to interpret the data and enable experimental measurements of the electron-velocity distribution function over several orders of magnitude without assumptions about its mathematical form. Major components of the instrument include (1) a reflective collection objective that gathers light over a range of 120° × 12°; (2) a spatial-filter image relay for measurement localization; (3) cylindrical optics for producing a line image of the collection aperture; (4) a transmission grating spectrometer; and (5) a time-gated, image-intensified camera. Thomson-scattered light collected from an ~(50 – μM) 3 volume of plasma is recorded with 0.8-nm spectral and 1° angular resolution. Initial experiments examined the properties of the electron-velocity distribution in gas-jet-produced plasmas in the presence of heating via inverse bremsstrahlung absorption.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Information theory optimization of signals from small-angle scattering measurements

Small-angle X-ray scattering (SAXS) of particles in solution informs on the conformational states and assemblies of biological macromolecules (bioSAXS) outside of cryo- and solid-state conditions. In bioSAXS, the SAXS measurement under dilute conditions is resolution limited, and through an inverse Fourier transform, the measured SAXS intensities directly relate to the physical space occupied by the particles via the P (r)-distribution. Yet, this inverse transform of SAXS data has been historically cast as an ill-posed, ill-conditioned problem requiring an indirect approach. Here, we show that through the applications of matrix and information theories, the inverse transform of SAXS intensity data is a well-conditioned problem. The so-called ill-conditioning of the inverse problem is directly related to the Shannon number. By exploiting the oversampling enabled by modern detectors, a direct inverse Fourier transform of the SAXS data is possible, provided the recovered information does not exceed the Shannon number. The Shannon limit corresponds to the maximum number of significant singular values that can be recovered in a SAXS experiment, suggesting this relationship is a fundamental property of band-limited inverse integral transform problems. This correspondence reduces the complexity of the inverse problem to the Shannon limit and maximum dimension. We propose a hybrid scoring function using an information theory framework that assesses both the quality of the model-data fit as well as the quality of the recovered P (r)-distribution. The hybrid score utilizes the Akaike information criteria and Durbin-Watson statistic that considers parameter-model complexity, i.e., degrees of freedom, and the randomness of the model-data residuals. The described tests and findings extend the boundaries for bioSAXS by completing the information theory formalism initiated by Peter B. Moore to enable a quantitative measure of resolution in SAXS, robustly determine maximum dimension, and more precisely define the best parameter model appropriately representing the observed scattering data.

Rambo, Robert P. [Science and Technology Facilitie↗
DOE OSTI2025

Structure and Sulfur: Tuning the Viscoelastic and Surface Properties of Natural Keratin Fibers

Natural keratin fibers, such as wool, possess a complex hierarchical structure that governs their mechanical properties and surface energy. However, the extent to which these characteristics are influenced by combined contributions of structural variations (e.g., fiber diameter, intermediate filament (IF) packing) and chemical composition (e.g., disulfide bond density) remains poorly understood. In this study, we investigate wool fibers from five sheep breeds (Merino, Polwarth, Cheviot, Eider, and Devon) to elucidate how these factors influence viscoelasticity and surface interactions. Using a multimodal approach integrating interfacial and bulk characterization methods, including inverse gas chromatography (IGC), atomic force microscopy-infrared spectroscopy (AFM-IR), X-ray photoelectron spectroscopy (XPS), uniaxial tensile testing, and synchrotron small-angle X-ray scattering (SAXS), we show that the nanometer-thick 18-methyleicosanoic acid (18-MEA) layer is consistently present across all wool types and plays a key role in governing hydrophobicity and surface heterogeneity. A controlled isothermal treatment at 200 °C, designed to cleave disulfide bonds, results in a nearly 40% reduction in specific surface area across all fiber types, accompanied by a significant decrease in tensile strength and 80% reduction in elongation at break for Merino and Devon wool, but limited influence on the mechanical properties of Eider fibers. Furthermore, rate-dependent tensile testing within the elastic regime reveals distinct viscoelastic responses among the fiber types, suggesting that the sulfur-rich protein matrix surrounding IFs and its structure contribute actively to stress partitioning. Altogether, when combined with conclusions from SAXS measurements of IF spacing, our work offers compelling insights into the role of the keratin-associated protein (KAP) matrix in shaping wool fiber mechanics. Differences in mechanical behavior among wool types, despite similar IF spacing or sulfur content, highlight the importance of matrix composition and cross-linking density, suggesting that the molecular architecture of the KAP network may be a dominant factor in determining fiber performance.

X-ray scattering↗
DOE OSTI2025

Quasinormal Coupled-Mode Analysis of Dynamic Gain in Exceptional-Point Lasers

One of the key features of lasers operating near exceptional points (EPs) is that the gain medium can support an oscillating population inversion above a pump threshold, leading to self-modulated laser dynamics. This unusual behavior opens up new possibilities for frequency comb generation and temporal modulation. However, the dynamic population inversion couples signals with different frequencies and is difficult to capture using conventional temporal coupled-mode theory (TCMT) based on stationary saturable gain. In this paper, we develop a perturbative coupled-mode analysis framework to capture the spatial-temporal dynamics of near-EP lasers. By decomposing discrete frequency generation into multiple excitations of resonant modes, our analysis establishes a minimal physical model that translates the local distribution of dynamic population-inversion into a resonant modal interpretation of laser gain. Furthermore, this work enables the exploration of unique properties in this self-time-modulated systems, such as time-varying scattering and nonreciprocal transmission.

Absorption↗
DOE OSTI2026

Constrained variational optimization of counting-time allocation in sequential scattering measurements: Application to Bonse–Hart USANS

Sequential scattering measurements are often performed under a fixed experimental-time budget, even though the expected count rate varies strongly across the measured coordinate. When the dwell time at each measurement position can be controlled independently, this variation creates a general resource-allocation problem: how should the available time be distributed to minimize the uncertainty of the reconstructed profile? We formulate this problem as a constrained variational optimization for measurements governed by Poisson counting statistics. When each measurement is treated independently, minimizing the averaged squared relative uncertainty yields an inverse-square-root intensity allocation. The formulation is then generalized to include correlations between neighboring measurements and an instrumental resolution operator, leading to an allocation criterion that equalizes the marginal reduction in posterior uncertainty per unit measurement time. Bonse–Hart ultra-small-angle neutron scattering (USANS), in which reciprocal space is sampled sequentially through analyzer-angle stepping, provides an experimentally grounded application. Computational benchmarking shows that the optimized allocation outperforms uniform-time and constant-relative-error strategies, while application to an experimentally measured graphite USANS profile from the Spallation Neutron Source, using Poisson resampling under alternative schedules, demonstrates how counting time should be redistributed toward weak-intensity regions under an identical total duration. The resulting framework applies to sequential scattering and related scanning measurements whenever local dwell times are adjustable and directly determine the measurement uncertainties, and when the relevant correlation and instrumental-response models are available.

Tung, Chi-Huan [ORNL] (ORCID:0000000221972074)↗
DOE OSTI2025

Machine-learning-informed scattering correlation analysis of sheared colloids

We have carried out theoretical analysis, Monte Carlo simulations and machine-learning analysis to quantify microscopic rearrangements of dilute dispersions of spherical colloidal particles from coherent scattering intensity. Both monodisperse and polydisperse dispersions of colloids were created and underwent a rearrangement consisting of an affine simple shear and non-affine rearrangement using the Monte Carlo method. We calculated the coherent scattering intensity of the dispersions and the correlation function of intensity before and after the rearrangement and generated a large data set of angular correlation functions for varying system parameters, including number density, polydispersity, shear strain and non-affine rearrangement. Singular value decomposition of the data set shows the feasibility of machine-learning inversion from the correlation function for the polydispersity, shear strain and non-affine rearrangement using only three parameters. A Gaussian process regressor is then trained on the data set and can retrieve the affine shear strain, non-affine rearrangement and polydispersity with relative errors of 3%, 1% and 6%, respectively. Altogether, our model provides a framework for quantitative studies of both steady and non-steady microscopic dynamics of colloidal dispersions using coherent scattering methods.

Gaussian process regression↗
DOE OSTI2025

Incommensurate Transverse Peierls Transition and Signature of Chiral Charge Density Wave in EuAl 4

In one-dimensional quantum materials, electrons and lattices can undergo a Peierls transition, a translational symmetry-breaking instability traditionally understood through electron coupling to longitudinal acoustic phonons. Recently, this paradigm has been revised in topological semimetals, where transverse acoustic phonons couple to p-orbital electrons, giving rise to a transverse Peierls transition. Importantly, transverse Peierls transition-induced distortions can further break mirror or inversion symmetries, producing nematic or chiral charge density waves. Here, we report the experimental identification of an incommensurate transverse Peierls transition in EuAl 4 . Using meV-resolution inelastic x-ray scattering, we observe complete softening of a transverse acoustic phonon upon cooling, while the longitudinal acoustic mode remains unaffected. First-principles calculations reveal that the transverse Peierls transition wavevector coincides with a charge susceptibility peak connecting nested Dirac bands. Second harmonic generation confirms mirror symmetry breaking, supporting a chiral charge density wave stabilized by the transverse Peierls transition.

36 MATERIALS SCIENCE↗